code: def forecast_pipeline(
      # input
      df: pd.DataFrame,
      time_col=TIME_COL,
      value_col=VALUE_COL,
      date_format=None,
      tz=None,
      freq=None,
      train_end_date=None,
      anomaly_info=None,
      # model
      pipeline=None,
      regressor_cols=None,
      lagged_regressor_cols=None,
      estimator=SimpleSilverkiteEstimator(),
      hyperparameter_grid=None,
      hyperparameter_budget=None,
      n_jobs=COMPUTATION_N_JOBS,
      verbose=1,
      # forecast
      forecast_horizon=None,
      coverage=0.95,
      test_horizon=None,
      periods_between_train_test=None,
      agg_periods=None,
      agg_func=None,
      # evaluation
      score_func=EvaluationMetricEnum.MeanAbsolutePercentError.name,
      score_func_greater_is_better=False,
      cv_report_metrics=CV_REPORT_METRICS_ALL,
      null_model_params=None,
      relative_error_tolerance=None,
      # CV
      cv_horizon=None,
      cv_min_train_periods=None,
      cv_expanding_window=False,
      cv_use_most_recent_splits=False,
      cv_periods_between_splits=None,
      cv_periods_between_train_test=None,
      cv_max_splits=3,
  ):
      """Computation pipeline for end-to-end forecasting.
  
      Trains a forecast model end-to-end:
  
          1. checks input data
          2. runs cross-validation to select optimal hyperparameters e.g. best model
          3. evaluates best model on test set
          4. provides forecast of best model (re-trained on all data) into the future
  
      Returns forecasts with methods to plot and see diagnostics.
      Also returns the fitted pipeline and CV results.
  
      Provides a high degree of customization over training and evaluation parameters:
  
          1. model
          2. cross validation
          3. evaluation
          4. forecast horizon
  
      See test cases for examples.
  
      Parameters
      ----------
      df : `pandas.DataFrame`
          Timeseries data to forecast.
          Contains columns [`time_col`, `value_col`], and optional regressor columns
          Regressor columns should include future values for prediction
  
      time_col : `str`, default TIME_COL in constants.py
          name of timestamp column in df
  
      value_col : `str`, default VALUE_COL in constants.py
          name of value column in df (the values to forecast)
  
      date_format : `str` or None, default None
          strftime format to parse time column, eg ``%m/%d/%Y``.
          Note that ``%f`` will parse all the way up to nanoseconds.
          If None (recommended), inferred by `pandas.to_datetime`.
  
      tz : `str` or None, default None
          Passed to `pandas.tz_localize` to localize the timestamp
  
      freq : `str` or None, default None
          Frequency of input data. Used to generate future dates for prediction.
          Frequency strings can have multiples, e.g. '5H'.
          See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
          for a list of frequency aliases.
          If None, inferred by `pandas.infer_freq`.
          Provide this parameter if ``df`` has missing timepoints.
  
      train_end_date : `datetime.datetime`, optional, default None
          Last date to use for fitting the model. Forecasts are generated after this date.
          If None, it is set to the last date with a non-null value in
          ``value_col`` of ``df``.
  
      anomaly_info : `dict` or `list` [`dict`] or None, default None
          Anomaly adjustment info. Anomalies in ``df``
          are corrected before any forecasting is done.
  
          If None, no adjustments are made.
  
          A dictionary containing the parameters to
          `~greykite.common.features.adjust_anomalous_data.adjust_anomalous_data`.
          See that function for details.
          The possible keys are:
  
              ``"value_col"`` : `str`
                  The name of the column in ``df`` to adjust. You may adjust the value
                  to forecast as well as any numeric regressors.
              ``"anomaly_df"`` : `pandas.DataFrame`
                  Adjustments to correct the anomalies.
              ``"start_time_col"``: `str`, default START_TIME_COL
                  Start date column in ``anomaly_df``.
              ``"end_time_col"``: `str`, default END_TIME_COL
                  End date column in ``anomaly_df``.
              ``"adjustment_delta_col"``: `str` or None, default None
                  Impact column in ``anomaly_df``.
              ``"filter_by_dict"``: `dict` or None, default None
                  Used to filter ``anomaly_df`` to the relevant anomalies for
                  the ``value_col`` in this dictionary.
                  Key specifies the column name, value specifies the filter value.
              ``"filter_by_value_col""``: `str` or None, default None
                  Adds ``{filter_by_value_col: value_col}`` to ``filter_by_dict``
                  if not None, for the ``value_col`` in this dictionary.
              ``"adjustment_method"`` : `str` ("add" or "subtract"), default "add"
                  How to make the adjustment, if ``adjustment_delta_col`` is provided.
  
          Accepts a list of such dictionaries to adjust multiple columns in ``df``.
  
      pipeline : `sklearn.pipeline.Pipeline` or None, default None
          Pipeline to fit. The final named step must be called "estimator".
          If None, will use the default Pipeline from
          `~greykite.framework.pipeline.utils.get_basic_pipeline`.
  
      regressor_cols : `list` [`str`] or None, default None
          A list of regressor columns used in the training and prediction DataFrames.
          It should contain only the regressors that are being used in the grid search.
          If None, no regressor columns are used.
          Regressor columns that are unavailable in ``df`` are dropped.
  
      lagged_regressor_cols : `list` [`str`] or None, default None
          A list of additional columns needed for lagged regressors in the training and prediction DataFrames.
          This list can have overlap with ``regressor_cols``.
          If None, no additional columns are added to the DataFrame.
          Lagged regressor columns that are unavailable in ``df`` are dropped.
  
      estimator : instance of an estimator that implements `greykite.algo.models.base_forecast_estimator.BaseForecastEstimator`
          Estimator to use as the final step in the pipeline.
          Ignored if ``pipeline`` is provided.
  
      forecast_horizon : `int` or None, default None
          Number of periods to forecast into the future. Must be > 0.
          If None, default is determined from input data frequency
  
      coverage : `float` or None, default=0.95
          Intended coverage of the prediction bands (0.0 to 1.0)
          If None, the upper/lower predictions are not returned
          Ignored if `pipeline` is provided. Uses coverage of the ``pipeline`` estimator instead.
  
      test_horizon : `int` or None, default None
          Numbers of periods held back from end of df for test.
          The rest is used for cross validation.
          If None, default is forecast_horizon. Set to 0 to skip backtest.
  
      periods_between_train_test : `int` or None, default None
          Number of periods for the gap between train and test data.
          If None, default is 0.
  
      agg_periods : `int` or None, default None
          Number of periods to aggregate before evaluation.
  
          Model is fit and forecasted on the dataset's original frequency.
  
          Before evaluation, the actual and forecasted values are aggregated,
          using rolling windows of size ``agg_periods`` and the function
          ``agg_func``. (e.g. if the dataset is hourly, use ``agg_periods=24, agg_func=np.sum``,
          to evaluate performance on the daily totals).
  
          If None, does not aggregate before evaluation.
  
          Currently, this is only used when calculating CV metrics and
          the R2_null_model_score metric in backtest/forecast. No pre-aggregation
          is applied for the other backtest/forecast evaluation metrics.
  
      agg_func : callable or None, default None
          Takes an array and returns a number, e.g. np.max, np.sum.
  
          Defines how to aggregate rolling windows of actual and predicted values
          before evaluation.
  
          Ignored if ``agg_periods`` is None.
  
          Currently, this is only used when calculating CV metrics and
          the R2_null_model_score metric in backtest/forecast. No pre-aggregation
          is applied for the other backtest/forecast evaluation metrics.
  
      score_func : `str` or callable, default ``EvaluationMetricEnum.MeanAbsolutePercentError.name``
          Score function used to select optimal model in CV.
          If a callable, takes arrays ``y_true``, ``y_pred`` and returns a float.
          If a string, must be either a
          `~greykite.common.evaluation.EvaluationMetricEnum` member name
          or `~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`.
  
      score_func_greater_is_better : `bool`, default False
          True if ``score_func`` is a score function, meaning higher is better,
          and False if it is a loss function, meaning lower is better.
          Must be provided if ``score_func`` is a callable (custom function).
          Ignored if ``score_func`` is a string, because the direction is known.
  
      cv_report_metrics : `str`, or `list` [`str`], or None, default `~greykite.common.constants.CV_REPORT_METRICS_ALL`
          Additional metrics to compute during CV, besides the one specified by ``score_func``.
  
              - If the string constant `greykite.framework.constants.CV_REPORT_METRICS_ALL`,
                computes all metrics in ``EvaluationMetricEnum``. Also computes
                ``FRACTION_OUTSIDE_TOLERANCE`` if ``relative_error_tolerance`` is not None.
                The results are reported by the short name (``.get_metric_name()``) for ``EvaluationMetricEnum``
                members and ``FRACTION_OUTSIDE_TOLERANCE_NAME`` for ``FRACTION_OUTSIDE_TOLERANCE``.
                These names appear in the keys of ``forecast_result.grid_search.cv_results_``
                returned by this function.
              - If a list of strings, each of the listed metrics is computed. Valid strings are
                `~greykite.common.evaluation.EvaluationMetricEnum` member names
                and `~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`.
  
                For example::
  
                  ["MeanSquaredError", "MeanAbsoluteError", "MeanAbsolutePercentError", "MedianAbsolutePercentError", "FractionOutsideTolerance2"]
  
              - If None, no additional metrics are computed.
  
      null_model_params : `dict` or None, default None
          Defines baseline model to compute ``R2_null_model_score`` evaluation metric.
          ``R2_null_model_score`` is the improvement in the loss function relative
          to a null model. It can be used to evaluate model quality with respect to
          a simple baseline. For details, see
          `~greykite.common.evaluation.r2_null_model_score`.
  
          The null model is a `~sklearn.dummy.DummyRegressor`,
          which returns constant predictions.
  
          Valid keys are "strategy", "constant", "quantile".
          See `~sklearn.dummy.DummyRegressor`. For example::
  
              null_model_params = {
                  "strategy": "mean",
              }
              null_model_params = {
                  "strategy": "median",
              }
              null_model_params = {
                  "strategy": "quantile",
                  "quantile": 0.8,
              }
              null_model_params = {
                  "strategy": "constant",
                  "constant": 2.0,
              }
  
          If None, ``R2_null_model_score`` is not calculated.
  
          Note: CV model selection always optimizes ``score_func`, not
          the ``R2_null_model_score``.
  
      relative_error_tolerance : `float` or None, default None
          Threshold to compute the ``Outside Tolerance`` metric,
          defined as the fraction of forecasted values whose relative
          error is strictly greater than ``relative_error_tolerance``.
          For example, 0.05 allows for 5% relative error.
          If `None`, the metric is not computed.
  
      hyperparameter_grid : `dict`, `list` [`dict`] or None, default None
          Sets properties of the steps in the pipeline,
          and specifies combinations to search over.
          Should be valid input to `sklearn.model_selection.GridSearchCV` (param_grid)
          or `sklearn.model_selection.RandomizedSearchCV` (param_distributions).
  
          Prefix transform/estimator attributes by the name of the step in the pipeline.
          See details at: https://scikit-learn.org/stable/modules/compose.html#nested-parameters
  
          If None, uses the default pipeline parameters.
  
      hyperparameter_budget : `int` or None, default None
          Max number of hyperparameter sets to try within the ``hyperparameter_grid`` search space
  
          Runs a full grid search if ``hyperparameter_budget`` is sufficient to exhaust full
          ``hyperparameter_grid``, otherwise samples uniformly at random from the space.
  
          If None, uses defaults:
  
              * full grid search if all values are constant
              * 10 if any value is a distribution to sample from
  
      n_jobs : `int` or None, default `~greykite.framework.constants.COMPUTATION_N_JOBS`
          Number of jobs to run in parallel
          (the maximum number of concurrently running workers).
          ``-1`` uses all CPUs. ``-2`` uses all CPUs but one.
          ``None`` is treated as 1 unless in a `joblib.Parallel` backend context
          that specifies otherwise.
  
      verbose : `int`, default 1
          Verbosity level during CV.
          if > 0, prints number of fits
          if > 1, prints fit parameters, total score + fit time
          if > 2, prints train/test scores
  
      cv_horizon : `int` or None, default None
          Number of periods in each CV test set
          If None, default is ``forecast_horizon``.
          Set either ``cv_horizon`` or ``cv_max_splits`` to 0 to skip CV.
  
      cv_min_train_periods : `int` or None, default None
          Minimum number of periods for training each CV fold.
          If cv_expanding_window is False, every training period is this size
          If None, default is 2 * ``cv_horizon``
  
      cv_expanding_window : `bool`, default False
          If True, training window for each CV split is fixed to the first available date.
          Otherwise, train start date is sliding, determined by ``cv_min_train_periods``.
  
      cv_use_most_recent_splits: `bool`, default False
          If True, splits from the end of the dataset are used.
          Else a sampling strategy is applied. Check
          `~greykite.sklearn.cross_validation.RollingTimeSeriesSplit._sample_splits`
          for details.
  
      cv_periods_between_splits : `int` or None, default None
          Number of periods to slide the test window between CV splits
          If None, default is ``cv_horizon``
  
      cv_periods_between_train_test : `int` or None, default None
          Number of periods for the gap between train and test in a CV split.
          If None, default is ``periods_between_train_test``.
  
      cv_max_splits : `int` or None, default 3
          Maximum number of CV splits.
          Given the above configuration, samples up to max_splits train/test splits,
          preferring splits toward the end of available data. If None, uses all splits.
          Set either ``cv_horizon`` or ``cv_max_splits`` to 0 to skip CV.
  
      Returns
      -------
      forecast_result : :class:`~greykite.framework.pipeline.pipeline.ForecastResult`
          Forecast result. See :class:`~greykite.framework.pipeline.pipeline.ForecastResult`
          for details.
  
              * If ``cv_horizon=0``, ``forecast_result.grid_search.best_estimator_``
                and ``forecast_result.grid_search.best_params_`` attributes are defined
                according to the provided single set of parameters. There must be a single
                set of parameters to skip cross-validation.
              * If ``test_horizon=0``, ``forecast_result.backtest`` is None.
      """
      if hyperparameter_grid is None or hyperparameter_grid == []:
          hyperparameter_grid = {}
      # When hyperparameter_grid is a singleton list, unlist it
      if (
          isinstance(hyperparameter_grid, list)
          and len(hyperparameter_grid) == 1
      ):
          hyperparameter_grid = hyperparameter_grid[0]
  
      # Loads full dataset
      ts = UnivariateTimeSeries()
      ts.load_data(
          df=df,
          time_col=time_col,
          value_col=value_col,
          freq=freq,
          date_format=date_format,
          tz=tz,
          train_end_date=train_end_date,
          regressor_cols=regressor_cols,
          lagged_regressor_cols=lagged_regressor_cols,
          anomaly_info=anomaly_info,
      )
  
      # Splits data into training and test sets. ts.df uses standardized column names
      if test_horizon == 0:
          train_df = ts.fit_df
          train_y = ts.fit_y
          test_df = pd.DataFrame(columns=list(df.columns))
      else:
          # Make sure to refit best_pipeline appropriately
          train_df, test_df, train_y, test_y = train_test_split(
              ts.fit_df,
              ts.fit_y,
              train_size=ts.fit_df.shape[0]
              - test_horizon
              - periods_between_train_test,
              test_size=test_horizon + periods_between_train_test,
              shuffle=False,
          )  # this is important since this is timeseries forecasting!
      log_message(
          f"Train size: {train_df.shape[0]}. Test size: {test_df.shape[0]}",
          LoggingLevelEnum.INFO,
      )
  
      # Defines default training pipeline
      if pipeline is None:
          pipeline = get_basic_pipeline(
              estimator=estimator,
              score_func=score_func,
              score_func_greater_is_better=score_func_greater_is_better,
              agg_periods=agg_periods,
              agg_func=agg_func,
              relative_error_tolerance=relative_error_tolerance,
              coverage=coverage,
              null_model_params=null_model_params,
              regressor_cols=ts.regressor_cols,
              lagged_regressor_cols=ts.lagged_regressor_cols,
          )
  
      # Searches for the best parameters, and refits model with selected parameters on the entire training set
      if cv_horizon == 0 or cv_max_splits == 0:
          # No cross-validation. Only one set of hyperparameters is allowed.
          try:
              if len(ParameterGrid(hyperparameter_grid)) > 1:
                  raise ValueError(
                      "CV is required to identify the best model because there are multiple options "
                      "in `hyperparameter_grid`. Either provide a single option or set `cv_horizon` and `cv_max_splits` "
                      "to nonzero values."
                  )
          except TypeError:  # Parameter value is not iterable
              raise ValueError(
                  "CV is required to identify the best model because `hyperparameter_grid` contains "
                  "a distribution. Either remove the distribution or set `cv_horizon` and `cv_max_splits` "
                  "to nonzero values."
              )
  
          # Fits model to entire train set. Params must be set manually since it's not done by grid search
          params = {
              k: v[0] for k, v in hyperparameter_grid.items()
          }  # unpack lists, `v` is a singleton list with the parameter value
          best_estimator = pipeline.set_params(**params).fit(
              train_df, train_y
          )
  
          # Wraps this model in a dummy RandomizedSearchCV object to return the backtest model
          grid_search = get_hyperparameter_searcher(
              hyperparameter_grid=hyperparameter_grid,
              model=pipeline,
              cv=None,  # no cross-validation
              hyperparameter_budget=hyperparameter_budget,
              n_jobs=n_jobs,
              verbose=verbose,
              score_func=score_func,
              score_func_greater_is_better=score_func_greater_is_better,
              cv_report_metrics=cv_report_metrics,
              agg_periods=agg_periods,
              agg_func=agg_func,
              relative_error_tolerance=relative_error_tolerance,
          )
          # Sets relevant attributes. Others are undefined (cv_results_, best_score_, best_index_, scorer_, refit_time_)
          grid_search.best_estimator_ = best_estimator
          grid_search.best_params_ = params
          grid_search.n_splits_ = 0
      else:
          # Defines cross-validation splitter
          cv = RollingTimeSeriesSplit(
              forecast_horizon=cv_horizon,
              min_train_periods=cv_min_train_periods,
              expanding_window=cv_expanding_window,
              use_most_recent_splits=cv_use_most_recent_splits,
              periods_between_splits=cv_periods_between_splits,
              periods_between_train_test=cv_periods_between_train_test,
              max_splits=cv_max_splits,
          )
  
          # Defines grid search approach for CV
          grid_search = get_hyperparameter_searcher(
              hyperparameter_grid=hyperparameter_grid,
              model=pipeline,
              cv=cv,
              hyperparameter_budget=hyperparameter_budget,
              n_jobs=n_jobs,
              verbose=verbose,
              score_func=score_func,
              score_func_greater_is_better=score_func_greater_is_better,
              cv_report_metrics=cv_report_metrics,
              agg_periods=agg_periods,
              agg_func=agg_func,
              relative_error_tolerance=relative_error_tolerance,
          )
          grid_search.fit(train_df, train_y)
          best_estimator = grid_search.best_estimator_
  
      # Evaluates historical performance, fits model to all data (train+test)
      if test_horizon > 0:
          backtest_train_end_date = train_df[TIME_COL].max()
          # Uses pd.date_range because pd.Timedelta does not work for complicated frequencies e.g. "W-MON"
          backtest_test_start_date = pd.date_range(
              start=backtest_train_end_date,
              periods=periods_between_train_test
              + 2,  # Adds 2 as start parameter is inclusive
              freq=ts.freq,
          )[-1]
          backtest = get_forecast(
              df=ts.fit_df,  # Backtest needs to happen on fit_df, not on the entire df
              trained_model=best_estimator,
              train_end_date=backtest_train_end_date,
              test_start_date=backtest_test_start_date,
              forecast_horizon=test_horizon,
              xlabel=time_col,
              ylabel=value_col,
              relative_error_tolerance=relative_error_tolerance,
          )
          best_pipeline = clone(
              best_estimator
          )  # Copies optimal parameters
          best_pipeline.fit(
              ts.fit_df, ts.y
          )  # Refits this model on entire training dataset
      else:
          backtest = None  # Backtest training metrics are the same as forecast training metrics
          best_pipeline = (
              best_estimator  # best_model is already fit to all data
          )
  
      # Makes future predictions
      periods = forecast_horizon + periods_between_train_test
      future_df = ts.make_future_dataframe(
          periods=periods, include_history=True
      )
  
      forecast_train_end_date = ts.train_end_date
      # Uses pd.date_range because pd.Timedelta does not work for complicated frequencies e.g. "W-MON"
      forecast_test_start_date = pd.date_range(
          start=forecast_train_end_date,
          periods=periods_between_train_test
          + 2,  # Adds 2 as start parameter is inclusive
          freq=ts.freq,
      )[-1]
      forecast = get_forecast(
          df=future_df,
          trained_model=best_pipeline,
          train_end_date=forecast_train_end_date,
          test_start_date=forecast_test_start_date,
          forecast_horizon=forecast_horizon,
          xlabel=time_col,
          ylabel=value_col,
          relative_error_tolerance=relative_error_tolerance,
      )
  
      result = ForecastResult(
          timeseries=ts,
          grid_search=grid_search,
          model=best_pipeline,
          backtest=backtest,
          forecast=forecast,
      )
      return result
  
imports: import functools
  import warnings
  from dataclasses import dataclass
  import pandas
  from sklearn import clone
  from sklearn.model_selection import ParameterGrid
  from sklearn.model_selection import RandomizedSearchCV
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
  from greykite.common.python_utils import get_integer
  from greykite.common.time_properties import min_gap_in_seconds
  from greykite.framework.constants import COMPUTATION_N_JOBS
  from greykite.framework.constants import CV_REPORT_METRICS_ALL
  from greykite.framework.input.univariate_time_series import UnivariateTimeSeries
  from greykite.framework.output.univariate_forecast import UnivariateForecast
  from greykite.framework.pipeline.utils import get_basic_pipeline
  from greykite.framework.pipeline.utils import get_default_time_parameters
  from greykite.framework.pipeline.utils import get_forecast
  from greykite.framework.pipeline.utils import get_hyperparameter_searcher
  from greykite.sklearn.cross_validation import RollingTimeSeriesSplit
  from greykite.sklearn.estimator.simple_silverkite_estimator import SimpleSilverkiteEstimator
label_desc: Use image node_id 2 for calling a global function with example usage: forecast_pipeline(df, time_col, value_col, date_format, tz, freq, train_end_date, anomaly_info, pipeline, regressor_cols, lagged_regressor_cols, estimator, hyperparameter_grid, hyperparameter_budget, n_jobs, verbose, forecast_horizon, coverage, test_horizon, periods_between_train_test, agg_periods, agg_func, score_func, score_func_greater_is_better, cv_report_metrics, null_model_params, relative_error_tolerance, cv_horizon, cv_min_train_periods, cv_expanding_window, cv_use_most_recent_splits, cv_periods_between_splits, cv_periods_between_train_test, cv_max_splits) and returns: result